Deep neural network predictions for excitation functions of 165Ho(α,xn) reactions

Türeci R., SARPÜN İ. H., Aydin A., ÇAKMAK Ş. M.

Applied Radiation and Isotopes, cilt.225, 2025 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 225
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.apradiso.2025.112075
  • Dergi Adı: Applied Radiation and Isotopes
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aquatic Science & Fisheries Abstracts (ASFA), Chemical Abstracts Core, Chimica, Compendex, EMBASE, Food Science & Technology Abstracts, INSPEC, MEDLINE, Pollution Abstracts
  • Anahtar Kelimeler: 165Ho(α,xn) reactions, Deep neural network, Excitation functions, Pythorch, effect of activation functions, Talys code
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Accurate nuclear reaction cross section data are essential for nuclear medicine, reactor technology, and nuclear astrophysics. In this study, excitation functions for 165Ho(α,n)168Tm, 165Ho(α,2n)167Tm, 165Ho(α,3n)166Tm and 165Ho(α,4n)165Tm reactions are analyzed over a wide range of alpha incident energies. Experimental data from EXFOR are compared with theoretical predictions generated using the TALYS nuclear reaction code and the TENDL-2023 evaluated nuclear data library. Additionally, a data-driven approach utilizing Deep Neural Networks (DNNs) with various activation functions (ReLU, ELU, LeakyReLU, SiLU, Mish, PReLU) is developed to predict the cross sections. Python programming language and pytorch module are used in the DNN predictions. The results demonstrate that while conventional models provide a reasonable approximation of reaction trends, Artificial Neural Network (ANN) models which are a branch of machine learning significantly improve agreement with experimental data. These findings underscore the potential of artificial intelligence as a complementary tool for enhancing nuclear reaction modeling. In addition, using different activation functions in the deep learning algorithm is important to get the best results in the predictions of the experimental data.